جستجوی مقالات مرتبط با کلیدواژه « الگوریتم ژنتیک » در نشریات گروه « صنایع »

application of Reliability can be seen in many industrial, communication, In the early stages of system design, many system features such as reliability, weight, cost, and etc are associated with uncertainty due to various reasons such as lifespan, operational conditions, etc. Since the use of the probabilistic approach in solving reliability problems has limitations and can only be used in quantitative analysis of information and in many cases does not produce useful and sufficient results for experts, therefore the use of the approach Fuzzy is much more efficient for solving reliability optimization problems. One of the ways to optimize the reliability is to allocate redundancy. When using redundant components in a subsystem, how the redundant components are used is particular importance. In reliability-redundancy allocation problems, the reliability of components is not known in advance and is considered as a decision variable. In the current research, the reliability-redundancy allocation problem has been investigated with two active and cold-standby redundancy strategies and the triangular fuzzy number approach has been used in using the parameters of probability functions and reliability calculation in two model problems and an industrial system. Genetic algorithm has been used to solve the problem. In the implementation of the genetic algorithm, the the random, tournament and roulette wheel methods has been used to select parents and different types of mutation and crossover operators have been used to produce children. The results are more efficient than the results obtained from solving the deterministic model.

Quadcopters are a special type of unmanned drones that have many applications in today's world. Due to limited resources, the design of a system must be done in such a way as to achieve the highest possible amount of reliability based on our limited resources.For this purpose, first the reliability of each subsystem was calculated. Then the reliability was optimized using computer algorithms. One of the conventional methods of increasing the reliability of systems is to use redundancies, but due to its limitations Finance and mass for quadcopters, we cannot use any number of extras to increase reliability. Therefore, optimization should be used. The most famous meta-heuristic algorithms can be mentioned as Genetic Algorithm, Coco, Ant Colony, Gray Wolf, etc.With the help of the firefly algorithm and reliability model, the quadcopter was checked in the presence of redundancy in terms of cost and mass minimization and having the most optimal reliability, and the resulting results were validated by genetic algorithm.

This paper presents an intelligent method for applying Data Envelopment Analysis (DEA) to design a sustainable supply chain.

In the proposed method, for defuzzification of the ERM model, we used the -cutting technique. Then, to measure the productivity in the presence of environmental uncertainty via different -levels, a genetic algorithm is implemented to find an optimal -cutting. Finally, an intelligent DEA model for ranking the supplier companies via optimal  value is designed.

This paper presents a new fuzzy DEA model based on a Genetic Algorithm for evaluating the productivity of suppliers in a sustainable supply chain.

Originality/Value: 

In the proposed method, since the -cut obtained from the Genetic Algorithm is optimal, there is no longer a need to calculate the efficiency for different α-cuts through trial and error. Therefore, the proposed method's advantage is that it offers a more sustainable ranking in addition to increasing productivity for each supplier. The example presented in this article demonstrates the method's superiority and advantages.

Today, changes in the volume and type of customer demand are a serious and signi cant problem for manufacturing companies. To address this problem, new production systems, including the dynamic cellular manufacturing system, have provided some solutions. In this system, the layout of machines can be changed from one period to another according to changes in demand. On the other hand, in the problem of scheduling parts in the cellular manufacturing system, the relocation of machines is usually done between two periods. Still, no time is considered for this relocation, and it is necessary to consider this time to determine the completion time of parts exactly. This paper introduces an innovative mathematical model to address the scheduling challenges in a cellular manufacturing system with continuous periods. The proposed model allows for dynamic machine relocation and layout changes within each period while taking into account the associated time and cost factors involved in the movement process. The possibility of machine relocation during the period can increase the system's dynamics. In the proposed model, cell formation coincides with scheduling. Other features of the model include alternative processing routes and the existence of identical versions of a machine. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. The objective of the proposed model is to minimize the total costs of completion time, machine relocation, and intracellular and intercellular material handling. Validation of the proposed model is performed in ve steps. The results of examining the features of the proposed model show that the model can eectively reduce completion time and other costs. Finally, to solve the model in larger sizes, two meta-heuristic algorithms of simulated annealing (SA) and genetic algorithm (GA) have been designed, and the obtained results have been compared with the results of CPLEX solver.

To locate hospitals, several points including the population of the area under the covering, transportation costs, and physical distance among the hospitals are very important. In the present study, health service networks were classi ed into two levels of low-level hospitals (provision of public health services) and high-level hospitals (providing specialized health services). In high-level hospitals, patients require professional services, and in the low-level ones, hospitals do not have the power to respond to specialized health services demands. They refer the patients to high-level hospitals in the case of patient visits or in emergency situations by ambulance. In the present case, patients are divided into two categories including the high priority (the category in which immediate service delivery is needed) and low priority. Regarding this problem, a stochastic robust dynamic mathematical model for location and allocation of health network regarding limited capacity and disturbance is developed which tries to reduce the total costs including the real features of a real problem such as limited capacity. The limited capacity of hospitals revealed that the health network needed rede nition of dierent layers for the network in the disturbance situation. In this study, we try to reduce the total costs by reducing costs of hospitals and costs such as transportation and service to patients. To solve the model, two metaheuristic algorithms including Non-dominated Sorting Genetic Algorithm II (NSGAII) and Particle Swarm Optimization (PSO) are applied. Taguchi method design is applied to minimize the cost of parameter tuning including the level of factors related to the proposed. The results demonstrated the applicability of the model to large-sized problems. For example, the total cost is minimized in conditions that are considered in the genetic algorithm, the population parameter at the highest level (150) and the intersection parameters, and the probability of mutation at the lowest level (0.7 and 0.1).

Reliability is an important indicator that should be taken into account when making strategic decisions in the design of the supply chain so that the system continues to function with the least loss when a member malfunctions or fails. In this article, an attempt is made to reduce the long-term costs of the chain and increase the service level by increasing the reliability. One of the innovations of this article is to present a new method for calculating the supply chain reliability index according to the definition and nature of reliability. Calculating the reliability of each of the levels with the calculation method of parallel systems is not correct, according to the proof in the text of the article, and the reliability of each member should be based on the amount of product or raw materials that each member supplies from that level. Produce or maintain, be different that these cases were not considered in the previous research. Value is the final indicator of the success of a system. In this article, the value of the supply chain is defined using three indicators of the SCOR model, including cost, responsiveness (delivery time) and reliability. The proposed model belongs to the category of non-linear integer programming problems (MINLP), which is designed with the aim of maximizing the value of the supply chain. Due to the complexity of the problem in large dimensions and proving that it is NP-hard, the meta-heuristic algorithm developed by genetics has been used to solve it.

One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption.

In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article.

The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems.

Originality/Value: 

In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a RSSP model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.

The hotel industry has become a competitive industry at the international level in recent decades, and countries have tended to use developed models and new techniques, and provide innovations to maximize income from it. As a result, it is critical to pay attention to how we can manage hotel income while noticing travel and passenger transportation costs and use modeling compatible with this field to optimize goal achievement.

The problems of optimizing hotel revenue management, passenger cost management, and analyzing how to expand the transportation used by them have been studied in this research. One of the key issues studied is to predict how to transport a passenger and choose its type based on different modes of travel such as air, rail, water, and road based on the amount of the passenger’s budget.

Many effective factors and criteria have been considered in the modeling done, and the amount of hotel reception capacity in the selected cities of travelers and the provision of various types of rooms with different pricing, and the examination of elements related to the services provided to travelers by the hotel and different accesses of the hotel, which is based on the hotel’s revenue model, affect on. It is useful to estimate the state of competitive factors of hotels.  Noteworthy, the transfer and mode of transportation have been determined to predict the level of demand for hotel reservations for all types of travelers during different periods in different tourism seasons. This subject is based on the traveler’s budget allocated for paying expenses during the travel pattern and the related results extracted from the estimated income model, as well as the influencing factors in choosing the hotel and transportation.

Originality/Value: 

In the current study, the design of NP-Hard problems led to the use of exact methods in small-sized problems and two multi-objective meta-heuristic algorithms, namely NSGA-II and MOPSO, in medium- and large-sized problems. The computation results show that the proposed algorithms are efficient and suitable methods for problem-solving.

In this article with title "Economic-statistical design of Bayesian control chart based on the predictive distribution for individual observations with an exponential qualitative characteristic distribution" the economic-statistical design of the Bayesian control chart based on the predictive distribution for individual observations of the exponential qualitative characteristic distribution is presented. In doing this, two types of the conjugate prior distribution and Jeffrey’s distribution are considered, and based on the distribution of observations in phase I, the predictive distribution is determined. Then, using the economic model of Lorenzen and Vance, an economic-statistical design was obtained for the data. Optimal design parameters (sampling distance, sample size, and control limits) were determined using a genetic algorithm and sensitivity analysis was performed for different values of model parameters. The results of this approach have been compared with the results of the classical model. The results show that this method is more effective than the classical method

Blood supply chain management is one of the important elds in the health care system. The blood supply chain consists of two components: donor blood collection and blood product supply. Human blood is a scarce resource. It is only produced by human beings and there are currently no other products or alternative chemical processes that can be used to generate blood. Blood supply chain is dierent from other supply chains because (a) supply of blood to donors and (b) one unit of blood being mechanically separated into ve components. In this study, a two-stage stochastic model for locationinventory of blood, which is part of the supply chain of perishable goods, was studied. The proposed blood supply chain has three levels of blood donors, mobile and xed centers and consumption centers. This study investigates platelet component which is a highly perishable component. The decision is made to determine the location of collection and distribute blood facility as well as the optimal inventory control policy such as identifying the optimal ordering for facing lack of blood, which is the main challenge based on perishable blood issuing policy which is FIFO. The objective function of the problem includes minimizing the cost of establishing blood donation centers, the cost of allocating donors to xed and mobile centers, the cost of transporting blood units along the supply chain network, the cost of ordering, inventory holding costs, and shortages and wasting blood in blood centers and hospitals. The eciency in reducing network costs lies in making correct decisions on locating, allocation, and inventory. We preferred a three-level network with donors and collection station and demand points and presented a programing model for locating station and allocation donor to them and identifying inventory policy. Given NP-hardness of the model, two metaheuristic GA and PSO algorithms were employed.

Warehousing using cross-docking is a well-known approach to distribute the products and decrease the costs of inventories and logistics in supply chain. In a cross-docking system, the goods are delivered from the delivery loader and move directly to the shipping loader without storage in the warehouse or distribution center. The use of this system reduces manpower costs, inventory, as well as increasing customer response speed and better control of distribution operations. However, in this research, the plan of portable cross warehouse along with the use of fixed cross warehouse in order to reduce the costs of constructing a cross warehouse has been proposed. Therefore, by presenting a random mathematical model, it has been tried to determine the appropriate place to send and establish a portable and fixed cross warehouse, as well as planning to determine which type of warehouse to be used in each location, transportation costs. To minimize possible and due to the uncertainty of the amount of demand for goods to be transferred between the pickup and Delivery points, the fuzzy programming method has been used. Genetic and Gray Wolf Optimizer have been used to solve the proposed model. To validate the performance of these evolutionary algorithms, for small-scale problems, it is used the solvers of GAMS software. The results of optimization show that the grey wolf algorithm provides better performance from the aspect of running time and the value of objective function. The number of products sent to the portable cross dock is one of the main results of this research.

Sensor networks are a new generation of networks. In this paper, an algorithm has been proposed to improve the performance of the EAMMH algorithm. For this purpose, the evolutionary firefly algorithm and genetic algorithm that itself has been modeled after natural biological evolution modeling have been used, and instead of randomly choosing cluster heads, it works on the possible answers that have superior attributes and also have a higher survival rate. The results of comparing LEACH and EAMMH protocols and the proposed method in terms of the number of dead nodes compared to the number of execution times based on 50, 100, and 200 nodes indicates that the number of dead nodes for the simulation of LEACH protocol is almost equal to the number of dead nodes for the EAMMH protocol, but the proposed algorithm has approximately 10 percent less dead nodes, but by an increase in the number of nodes (200 nodes) the number of dead nodes have decreased 35 percent and 22 percent compared to LEACH and EAMMH algorithms respectively.

A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians.

The purpose of this paper is to propose an improved genetic algorithm to solve the problem of Uncapacitated Single-allocation Hub Location. Previous methods have paid less attention to the diversity of population, and due to insufficient vairation in mutation operators, they perform well only in a few runs, and in other runs they are caught in the local optimum.

The proposed method uses appropriate genetic operators to increase diversity of the population and performs local search around the best answer to exploit promising areas of the solution space. The use of hub mutation operators along with allocation mutation operators in the proposed algorithm has increased its exploration ability and effectiveness, which has led to discovery of the optimal answer in most runs for large size problems. Also, searching for the local neighborhood of the best answer made convergence faster and reduced the total running time for large instances.

Evaluation of the proposed method and base algorithm on the Australian Post (AP) dataset showed that the improvements increased efficiency of the genetic algorithm in achieving optimal solutions for problems as large as 200 nodes from 2% to more than 85%.

 This study showed that meta-heuristic algorithms and their improved versions are suitable methods for solving hub location problems in a short and limited time.

Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose of this paper is to compile a credit forecasting model for legal customers of private banks by using meta-heuristic algorithms in the branches of Pasargad Bank in the northwest of Iran.

This research is base on the purpose of developmental research and based on the method of performing descriptive work. The statistical population of this study is in two sections of banking experts and legal customers of Pasargad Bank in the northwest of the Iran. The statistical sample size for the first community of 58 banking experts including managers, credit officials and heads of branches in with credit work experience in private banks and for the second community, 427 legal clients were selected based on targeted sampling. In order to collect data in this research, a questionnaire and documents of Pasargad Bank have been used. The validity of the questionnaire was investigated as content validity and based on the indicators of content validity ratio and content validity index. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient. In order to analyze the research data, t-test, confirmatory factor analysis, multilayer neural network, genetically trained neural network, trained neural network with particle swarm optimization and trained neural network with differential evolution will be used.

 The research findings show that all four models are able to predict the credit predictions of the legal customers of private banks and the best way to predict the credit predictions of the legal customers of private banks is the neural network trained with differential evolution algorithm with the least amount of error compared to the other three methods.Originality/Value: In this research by using meta-heuristic algorithms, a new credit forecasting model produce for legal customers of private banks with the least amount of error.

The proposed control scheme in this paper is based on the design of an optimal load distribution of reactive power for High-Voltage Direct-Current systems (HVDCs). The purpose is to monitor and implement appropriate operations to create a balance in the power and eliminate the voltage or current that has exceeded the permissible limit. Two-terminal HVDC transmission lines are of the most important elements of the power system. The optimization of reactive power distribution for power systems is defined based on the minimization of power loss under equal and unequal constraints. The problem of optimizing the reactive power distribution in integrated AC-DC power systems in the form of HVDC lines is generalized with regard to the control specifications for power transmission. In this paper, the problem is solved using Genetic Optimization Algorithm (GA). The validation, efficiency and the effectiveness of the proposed method are measured by comparing the results of the GA and those obtained by previous researches. Results indicate the effectiveness of the proposed method in the optimization of power distribution and the design of HVDC lines.

This study analyzes the portfolio optimization model, by considering the financial management and investment science in order to evaluate risks and return in regard with restrictions such as buyers’ assets for purchasing per share. Accordingly, a novel model is designed as linear programming in order to optimize the investment portfolio, considering the expected rate of return, the minimum risk, and the buyer’s assets. After the introduction of the model as linear programming and expressing the related limitations, different types of investments which an investor can consider in order to form an investment portfolio were studied. Finally, an approach is proposed to solve the model by using the genetic algorithm, and is implemented and analyzed in regard with a real example. According to the results of this study, the new model reduced downside risk in comparison with previously proposed models, in a manner that its stair descent continues as the number of shares under study increases.

Today, the competitive business environment has led to increasing cooperation among companies as the members of supply chain networks. In this area, the supply chain logistics network design is an important subject due to its effect on the responsiveness and efficiency. Over the past few years, due to environmental issues, their legal requirements and economic benefits, great attention has been paid to inverse logistics. In this paper, a mixed integer stochastic location-inventory model has been proposed for the integrated design of the network of a multi-period multi-product closed loop supply chain considering multiple capacity levels for facilities. The market demand and correspondingly the return value are considered to be uncertain in order to make the model close to the real-life conditions. Since the capacitated facility location problem considered in this research is an NP-hard type problem, we have used two meta-heuristic algorithms including the genetic algorithm (GA) and the Bees algorithms (BA) for solving this problem. Some numerical problems are designed and solved to assess the performance of the model and solution heuristics. From the viewpoint of solution quality, the BA outperforms the GA (by an average of 11.6%) whilst from the viewpoint of solution time, the GA is five times faster than the BA on average.

A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians.